{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMLTLFeGU6o51D/Lg5n2B5M",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kimjaehwankimjaehwan/marketing/blob/main/Prospect_Theory.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###전망이론(Prospect Theory) 개요\n",
        "  - 전망이론은 심리학자 다니엘 카너먼(Daniel Kahneman)과 경제학자 아모스 트버스키(Amos Tversky)가 1979년에 제안한 행동경제학 이론입니다.\n",
        "  - 이 이론은 사람들이 위험이나 불확실한 상황에서 어떻게 결정을 내리는지 설명합니다.\n",
        "  - 특히, 사람들은 기대 효용 이론(Expected Utility Theory)이 제시하는 대로 합리적이지 않고, 감정과 심리에 의해 영향을 받는다는 것을 강조합니다.\n",
        "\n",
        "####주요 개념\n",
        "\n",
        "1. 가치 함수(Value Function):\n",
        "\n",
        " * 이 함수는 이득과 손실을 평가하는 데 사용됩니다.\n",
        " * 일반적으로 이 함수는 볼록형(이득에 대해)과 오목형(손실에 대해)으로, 이득보다 손실에 더 민감하게 반응합니다.\n",
        " * 이는 \"손실 회피(Loss Aversion)\"로 알려진 개념과 연결됩니다.\n",
        " * 손실에 대한 가치는 같은 크기의 이득보다 심리적으로 더 크게 느껴집니다.\n",
        "\n",
        "2. 확률 가중 함수(Probability Weighting Function):\n",
        "\n",
        " * 사람들은 실제 확률을 왜곡해서 지각합니다.\n",
        " * 낮은 확률은 과대평가하고, 높은 확률은 과소평가합니다.\n",
        " * 이는 사람들이 복권과 같은 낮은 확률의 이벤트에 과도한 기대를 가지는 이유를 설명합니다.\n",
        "\n",
        "####전망이론의 수식\n",
        "  \n",
        "![image.png]()\n",
        "\n",
        "####실제 사례\n",
        "\n",
        "1. 보험 가입:\n",
        "\n",
        " * 사람들은 낮은 확률로 발생하는 큰 손실을 피하기 위해 비싼 보험에 가입하는 경향이 있습니다.\n",
        " * 이는 낮은 확률의 손실을 과대평가하는 경향 때문입니다.\n",
        "\n",
        "2. 복권 구매:\n",
        "\n",
        " * 사람들은 매우 낮은 확률로 큰 이득을 얻을 수 있는 복권을 구매합니다.\n",
        " * 이는 낮은 확률의 이득을 과대평가하기 때문입니다.\n",
        "\n",
        "3. 투자 결정:\n",
        "\n",
        " * 투자자들은 잠재적인 손실을 피하기 위해 손실 난 주식을 지나치게 오래 보유하거나, 이익이 나는 주식을 너무 빨리 매도하는 경향이 있습니다.\n",
        " * 이는 손실 회피와 관련된 가치 함수의 비대칭성에서 기인합니다.\n",
        "\n",
        "#### 정리\n",
        "\n",
        "  - 전망이론은 전통적인 경제학 이론과 달리 인간의 비합리적인 행동을 설명하는 데 중점을 둡니다.\n",
        "  - 이론은 특히 손실 회피, 확률 왜곡, 비대칭적 가치 평가와 같은 요소들을 통해 실제 의사결정 과정을 설명합니다.\n",
        "  - 이 이론은 보험, 복권, 투자 등 일상적인 재무적 결정뿐만 아니라 다양한 비즈니스 전략에도 적용될 수 있습니다."
      ],
      "metadata": {
        "id": "loqF0FlUgHQb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. 가치 함수(Value Function)"
      ],
      "metadata": {
        "id": "_hhcM2kweJ05"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Google Colab에 NanumGothic 폰트 설치\n",
        "!apt-get update -qq\n",
        "!apt-get install fonts-nanum* -qq\n",
        "\n",
        "# matplotlib.font_manager 임포트\n",
        "import matplotlib.font_manager as fm\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 설치된 폰트 확인 및 matplotlib에 적용\n",
        "font_path = '/usr/share/fonts/truetype/nanum/NanumGothic.ttf'\n",
        "font = fm.FontProperties(fname=font_path)\n",
        "fm.fontManager.addfont(font_path)\n",
        "plt.rc('font', family='NanumGothic')\n",
        "\n",
        "print(\"폰트 설정 완료\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afDxbaJqeunx",
        "outputId": "e487ab28-0ec1-4782-bf6a-ecd7eddd2b4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "W: Skipping acquire of configured file 'main/source/Sources' as repository 'https://r2u.stat.illinois.edu/ubuntu jammy InRelease' does not seem to provide it (sources.list entry misspelt?)\n",
            "Selecting previously unselected package fonts-nanum.\n",
            "(Reading database ... 123597 files and directories currently installed.)\n",
            "Preparing to unpack .../fonts-nanum_20200506-1_all.deb ...\n",
            "Unpacking fonts-nanum (20200506-1) ...\n",
            "Selecting previously unselected package fonts-nanum-coding.\n",
            "Preparing to unpack .../fonts-nanum-coding_2.5-3_all.deb ...\n",
            "Unpacking fonts-nanum-coding (2.5-3) ...\n",
            "Selecting previously unselected package fonts-nanum-eco.\n",
            "Preparing to unpack .../fonts-nanum-eco_1.000-7_all.deb ...\n",
            "Unpacking fonts-nanum-eco (1.000-7) ...\n",
            "Selecting previously unselected package fonts-nanum-extra.\n",
            "Preparing to unpack .../fonts-nanum-extra_20200506-1_all.deb ...\n",
            "Unpacking fonts-nanum-extra (20200506-1) ...\n",
            "Setting up fonts-nanum-extra (20200506-1) ...\n",
            "Setting up fonts-nanum (20200506-1) ...\n",
            "Setting up fonts-nanum-coding (2.5-3) ...\n",
            "Setting up fonts-nanum-eco (1.000-7) ...\n",
            "Processing triggers for fontconfig (2.13.1-4.2ubuntu5) ...\n",
            "폰트 설정 완료\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 설정된 파라미터들\n",
        "alpha = 0.88  # 이득에 대한 감수성\n",
        "beta = 0.88   # 손실에 대한 감수성\n",
        "lambda_ = 2.25  # 손실 회피 계수\n",
        "\n",
        "# 값 생성\n",
        "x = np.linspace(-1, 1, 400)  # -1에서 1까지의 x 값 생성\n",
        "y_positive = x[x >= 0] ** alpha  # 이득 부분\n",
        "y_negative = -lambda_ * (-x[x < 0]) ** beta  # 손실 부분\n",
        "\n",
        "# 그래프 그리기\n",
        "plt.figure(figsize=(7, 6))\n",
        "plt.plot(x[x >= 0], y_positive, label=\"이득 (Gain)\", color=\"blue\")\n",
        "plt.plot(x[x < 0], y_negative, label=\"손실 (Loss)\", color=\"red\")\n",
        "plt.axhline(0, color='black', linewidth=0.5)\n",
        "plt.axvline(0, color='black', linewidth=0.5)\n",
        "plt.title(\"전망이론의 가치 함수\")\n",
        "plt.xlabel(\"결과 (Outcome)\")\n",
        "plt.ylabel(\"가치 (Value)\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "WyY7PtL5eaoJ",
        "outputId": "ed88a3f7-bc01-4028-de8e-31456ba2ade7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. 확률 가중 함수(Probability Weighting Function)"
      ],
      "metadata": {
        "id": "G4R5WvBkearB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 설정된 파라미터\n",
        "gamma = 0.61  # 확률 왜곡 정도\n",
        "\n",
        "# 확률 값 생성\n",
        "p = np.linspace(0, 1, 400)  # 0에서 1까지의 확률 값 생성\n",
        "pi_p = p ** gamma / (p ** gamma + (1 - p) ** gamma) ** (1 / gamma)  # 확률 가중치 계산\n",
        "\n",
        "# 그래프 그리기\n",
        "plt.figure(figsize=(7, 6))\n",
        "plt.plot(p, pi_p, label=\"확률 가중치 (Probability Weight)\", color=\"green\")\n",
        "plt.axhline(0, color='black', linewidth=0.5)\n",
        "plt.axvline(0, color='black', linewidth=0.5)\n",
        "plt.plot(p, p, '--', label=\"실제 확률 (Actual Probability)\", color=\"black\")\n",
        "plt.title(\"전망이론의 확률 가중 함수\")\n",
        "plt.xlabel(\"실제 확률 (Actual Probability)\")\n",
        "plt.ylabel(\"가중 확률 (Weighted Probability)\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "jrPeRDmyeauG",
        "outputId": "3fc0c3a0-2fbc-47e8-e79f-2e1e7f5cdf8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 설정된 파라미터들\n",
        "lambda_ = 2.25  # 손실 회피 계수\n",
        "\n",
        "# 값 생성\n",
        "x = np.linspace(-0.99, 0.99, 400)  # 로그 함수가 정의되지 않는 부분(-1 이상) 피함\n",
        "y_positive_log = np.log(1 + x[x >= 0])  # 이득 부분\n",
        "y_negative_log = -lambda_ * np.log(1 - x[x < 0])  # 손실 부분\n",
        "\n",
        "# 그래프 그리기\n",
        "plt.figure(figsize=(7, 6))\n",
        "plt.plot(x[x >= 0], y_positive_log, label=\"이득 (Gain)\", color=\"blue\")\n",
        "plt.plot(x[x < 0], y_negative_log, label=\"손실 (Loss)\", color=\"red\")\n",
        "plt.axhline(0, color='black', linewidth=0.5)\n",
        "plt.axvline(0, color='black', linewidth=0.5)\n",
        "plt.title(\"로그 함수를 적용한 전망이론의 가치 함수\")\n",
        "plt.xlabel(\"결과 (Outcome)\")\n",
        "plt.ylabel(\"가치 (Value)\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "gc9pVPLneaxo",
        "outputId": "34900b7f-2f89-46da-89a6-17e61dc73f2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x600 with 1 Axes>"
            ],
            "image/png": 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S8tNPP1G3bt1MbSveQT1xIi6oVasWkZGRTvfA1apVi88//zzZvXGZSeDAnCjW1fukOnbsyPr164mKimL58uVcvHiRb775hieeeMJRp1ixYk6JBJjzu914s7fNZmPFihVUrFgxxfdsNpvThKQXL14kODg4zfvhChQo4JjmIi3VqlXjnXfeoWjRoo6bwVN7tGvXzmn+tiFDhqR6f2JSO1N6z263U7p0acd+PvvsM0qWLEmdOnUICQlhyJAhTJ06NdWJhdPy7bffsmrVqmSfO0CRIkV4+eWX0x2BnGTZsmW0atWKcuXKkS9fPkqVKkX9+vWZMmUKy5Yt495773WqHxQURMuWLfniiy+cylevXk1QUFCaP5ctWrRIsQe1WLFigNnLl9L7lStXTnF/KSWZmXHmzBmaNm1KqVKlmDBhQoa2Xbt2LbVq1aJAgQKsXbuW2rVr8/333zNq1Cj69OlDZGRkhvbn6+vLsmXLeO6559i1axdr166lUqVK7Nu3z+nnqVy5ck496p07d071e9WuXTtGjhyZ6vtK4CQ96okTcQORkZFERUW5dMkryezZs/nggw8cvSUjR450ej/psuT1UrrpPaku4NLcW6GhockuNd5o8+bNdOnSJd19XS8sLIyzZ8+mWeeFF15g/PjxycpPnTrFhAkT2LBhA/v27XPM13XTTTdxzz330K5dO3r27JliAlWoUCEmTJjAhAkTSExMTPUzckXSPHXnzp2jS5curF+/PlP7GThwIF988QUjRozgk08+oWTJkly6dIkdO3YwduxYZsyYkWy1B4C3336bevXqUb16dTp06MDu3bvp27cvY8aMSfMy+5o1awC4fPkyMTExTj2XmXHt2jUuXrzIxYsXOX36NCdOnOCPP/4gLi4u3cvCSb766iv69evHww8/zJQpU1y+BQDg3XffZfr06YwfP95pbsCKFSuyY8cOevfuzcaNG5OtzJGeuLg4Xn75ZY4cOeIou/E7VqBAAQYNGuRUltr3KmnaE1e+dyIpUU+cSBpuvMfqxnvfUloa6Pr7rlJ7/8bHwYMHKVOmDH5+fslG2aVmx44djBs3LtX/xY8bN469e/fm5MeTqqTLaRlx5syZNEf/Tpo0KcVVIE6dOkVERARHjx5l1KhRHD9+nGvXrhEdHc0PP/zAI488wvvvv5/mJbTGjRuzffv2VBO4lCYkTktkZCQbNmxIs87bb7+dYkJz6dIlJk6cyOrVq+nVqxe33norwcHBlC1blkcffZQtW7Zw9epVlixZkmzbKlWqsHr1aiZNmsRNN93E008/zahRo3jyySddinvWrFn079/ftUZeJyIiwnE/pq+vL0WKFKFGjRp06dKFsWPH8uOPP1K4cGGXL4f+/PPPjuRz1qxZGZ6w9+mnn+bgwYNOCVyS4sWLs3z58gwncGD+jB45coQLFy6k+J2Lj4/njjvuSDayWSSnKP0XSUNq910l6dq1a4pTVyTdd9WoUaM01+28UVJvQ7NmzRy9I6kxDIOCBQum+r/4QoUKZSqZSk9iYiJ33nknx44dS/WSmZ+fX7bfPxcYGJhicrtp0yYCAwOTLZUVFBTEnXfeyZ133knJkiXp3bt3qtOU/Pnnn1y6dCnVY0+ZMiVLPXQp6dmzZ5rvpzZ4wtfXN81eqfr162d6rrGkhDmjtm7dSnx8PAEBAeTPnz/NnqVPP/003f3VrVuXY8eOpbtKSmqyMkFzWpI+m5CQkFTbGBwcnCPfO5GUKIkTScOQIUMYMmRIprfPyf+R+/j4EBUVlep8UpGRkS7ff5URMTEx/PHHH5w9e5bixYtn+/4zqkGDBsTFxfHUU0/Rs2dP7rzzTooUKUJCQgInT55k69atjBw5Ms1eoOtHH6ckoz1BSZ97dHR0mglX0hxm1ytatCh9+/alZcuWjBw5kgceeIBixYoRFRXFrl27GDt2LP7+/slWZsgOvr6+Lq0T6+fn59QuVyapzqjMJnA5Kek/LZcuXaJQoULJ3k+6HzMnvnciKdFPmoiHioiI4JVXXnFM5nvjY/Dgwclm709N0o3qrihUqBB33HEHpUqVSncpsc2bN2eliS4JCwtj586dBAcH06NHD0qWLIm/vz/58uWjevXqzJ07lxdeeIFZs2aluo969erRrl27TE+VcqPSpUtz6623EhISkur5yZ8/Pw899FCK20+cOJFRo0Yxe/ZsIiIiCAoKonz58rz22mu0atWKbdu2ERwcnKnPC8xeypTukbv77rvZtm1bmjHnz5+fUqVKZfuxrdpfRrYvUaIEYWFhlChRIsXPJTg4mL/++osqVaq4tL+MfO9EUuJjqN9XRDzA1q1b+eGHH9K8vA3m5d7o6GgCAwOzlOi4E7vdrt4dEUlGSZyIiIiIB9J/7UREREQ8kJI4EREREQ+k0anpsNvtnDp1ikKFCmXLDOQiIiIiaTEMg+joaMqUKZPm/bBK4tJx6tSpDM2iLyIiIpIdTpw4keYKKkri0pE0F9CJEycICQnJ1n3bbDbWrVtH06ZNvWqYudqtdnsLm81G06ZNWbdunVe13VvPudrtXe2GnGt70jKMKc1HeD0lcelIuoQaEhKSI0lccHAwISEhXvWDr3ar3d7CZrPh5+fndW331nOudntXuyHn257ebVwa2CAiIiLigZTEiYiIiHggJXEiIiIiHkj3xGUDwzBISEggMTExQ9vZbDb8/f25du1ahrf1ZJ7Sbj8/P/z9/TW1jIiIuCUlcVkUHx/Pv//+y9WrVzO8rWEYlCpVihMnTnhVouBJ7Q4ODqZ06dIEBgZaHYqIiIgTJXFZYLfbOXLkCH5+fpQpU4bAwMAMJSV2u52YmBgKFizoVYtbe0K7DcMgPj6ec+fOceTIESpUqOC2sYqIiHdSEpcF8fHx2O12wsPDCQ4OzvD2drud+Ph48uXL51UJgqe0O3/+/AQEBHDs2DFHvCIiIu7Cff+CehB3TkQka3RuRUTEXekvlIiIiIgHUhInIiIi4oGUxEmqatasycmTJ53KPvroI0qVKkWlSpVSfLz44osWRSsiIuJdlMR5sR9++IHatWtTpUoVatWqxbp165zej4+Px2azOZUdPnyYsWPHcujQoRQfH374oUvHXrZsGStXrnQq+/7772ndujXVqlUjIiKCatWq0ahRI+Lj413a5z///EPnzp0xDCPduoZh0LNnT+Li4lzat4iIiLvR6FQvdfbsWTp16sTKlSu5++67+f3332natClbtmyhfPnyqW5nGEaW53Y7c+YMK1eu5Msvv3SUjRo1iq+++orJkydz9913O8r/+ecfl+doCwsLY9GiRS7V9fHx4amnnuLNN99k1KhRGWuAiIiIG1ASl80MA1yd99duhytXwM8PsmMQZHAwuJpfLViwgA4dOjgSpsqVK/Pcc88xZcoUxo4dm+p2Pj4+WV5lYezYsTzxxBOO11u2bGHOnDn88ssvFCxY0KluWFhYlo6Vlvvvv5/hw4dz9uxZSpQokWPHERERyQm6nJrNrl6FggVde4SE+BIWVoSQEF+Xt0nrkZFFI3755Rfq16/vVPbQQw/x888/p7ldnTp1GDlyJFWrVk3x0aNHjzS3j4+PZ+3atTRq1MhRNn78eIYOHZosgUvJ2rVrqV27NtWqVaNq1ar06dOHhIQEAE6ePMltt93mqNuwYUOmT59OREQEVatWpUaNGmzevNlpf48//jizZs1K97giIiLuRj1xXurSpUsUKVLEqaxYsWKcO3cuze169OiRbqKWlm3btnHPPfc4XZL98ccf+eSTT1zavmjRoqxevZrixYuTkJBA69at+fTTT+nVqxc2m83pHjcfHx8mT57M2rVrKVmyJD/99BPt27fnyJEjBAUFAdCkSRO6d+/O0KFDM90mERERKyiJy2bBwRAT41pdu91OVFQUISEh2TKpbEYWjShWrBgXLlxwKjt16hSlS5dOsf7o0aOZM2eOy/vv3Lkzb731VrLyQ4cOUblyZaeyixcvUqxYMcfrBQsWOC7pxsTE0KdPH15++WUAateu7ajn7+9Pq1at2L17d6pxDBgwgJIlSwJQr149QkJC+N///ke1atUAKFeuHMePH3e5XSIi4t2OHYPFi+Gll6yORElctvPxgQIFXKtrt0Niolk/txcGqF+/Phs2bKBTp06OslWrVtG4ceMU6w8ePJjBgwdn+biXLl3ipptuciorWrQoFy5coGzZsgB07dqVrl27AvDWW29x+fJlR92TJ08yZswYfvrpJ6Kjo4mJieGhhx5K9Xjh4eFOr4sVK8bFixedylwZzSoiIt7r2jVYtgxmz4aNG8373yMi4Lo7gyyhe+K8VKdOndiyZQsrVqwAYP369SxYsIDevXvn6HGLFi3qlJQB1K1bl02bNqW7rc1mo0GDBhQrVoxly5Zx8OBBhgwZkuY2KY2kvTFpy+poWxERyXsMA3bvhn79oHRp6NIFNmwwyx96CPLntzpC9cR5rUKFCrFu3ToGDRrEa6+9Rvny5Vm7dm26ozTr1q2bLAlLEh0dTY8ePRg5cmSq21esWJG5c+c6lfXv358XXniBdu3akT+Nb8Wvv/5KcHAwb7zxhqPswIEDacabnqNHj3LzzTdnaR8iIpJ3XLgACxaYvW6//vpfeXg4PPUU9OgBt95qlt0wlWquUxLnxW655Ra++uqrDG2T1ujVNWvWMG3atDS3v++++3j++eedesOaNGlC27Ztady4MZMnT6Z69eqO9y5fvuwYtVqiRAnOnj3rmBLk559/ZtWqVTz44IMZasP11q9fT7NmzTK9vYiIeL7ERLOXbfZsWL4ckuaYDwqCRx+Fp5+GBx80pwRzJ0riJFuld39ZUFAQzZo1Y/369Tz22GOO8pEjR7J69WpeffVVTp486Zjgt3z58o6RozfffDPvv/++4x648uXLM3bsWFatWgVAQECAY9QpQGBgYLKJgoOCgpzKFixY4PIEwSIikrccP24mbrNnw4kT/5XXqAE9e8Ljj0NoqHXxpUdJnKQqICAAf//s/xF59dVX6d27t1MSB9CyZUtatmyZ5rZPP/00Tz/9tFNZ0uCMsmXL8tdffznKb1xGDGDjxo2O55s3b6ZOnTqUKlUqw20QERHPZLPBqlUwYwasWWPe4wZQtCh062ZeMq1Rw9oYXaUkTlK1Z8+eDNWvVq0azzzzTLr1SpUqRfv27fnqq6+SJXK5xTAMZs+ezfTp0y05voiI5K6//oKZM+HTT+H06f/KH3gAeveGdu0gXz6rosscJXGSbcqWLeuYJiQ97dq1IyQkJIcjSp2Pj0+yARYiIpK3xMWZU4PMmAHfffdfeYkSZo9bz55QoYJ18WWVkjgRERHJUw4eNBO3zz4zR5uCOY9rs2Zmr1vr1hAQYG2M2UFJnIiIiHi8q1fhiy/M5G3btv/Ky5Y1R5f27Am33GJdfDlBSZyIiIh4rF9/NRO3+fMhMtIs8/ODli3NXrfmzSEHxui5hTzaLBEREcmrYmPNXrcpU+D66UtvvdXscXvqKShTxrr4couSOBEREfEIf/wBU6eaI0yTlsH29zcn5O3d21wOK7fXIreSkjjJFLvdjq83fVNERMQSNhusXGn2ul031Sc33wzPPmve7+at033qr7AAZlL28ssvu1z/lltu4ULSkJ/rNGnShF+vX2xOREQkE/75B9580xyM8NhjZgLn42Pe6/b11/D33zBsmPcmcKAkTv7f6tWr+fjjjzl48GC6dePi4rh06RJFihRJ9p7NZsPmworAy5YtY+XKlQDccccdHDt2LMMxZ9SUKVPYunVrjh9HREQyx26HtWuhbVszeRsxAv7915zXbehQM3FbtQpatXK/dUytoCROOHjwIM8//zyTJ0+mc+fOHDlyJM36K1euJDExkbNnz2bqeGfOnGHlypW0adMGgPj4eJcSv6zq3bs37733HrGxsTl+LBERcd25czBmjDnxbvPmsGKFmdA1bAiLFpnrmr73HpQrZ3Wk7kX3xGU3wzAnq3GF3Q5Xrpj/nciO+8uCg82+ZhfFxsYyc+ZMxo4dy7x582jYsCHlypWjadOmPPPMMzzzzDMULlzYaZsrV67wzjvv0L59e3r27MnXX3+NXwb/OzR27FieeOKJDG2THfz9/WnTpg0zZsxgwIABuX58ERH5j2HA9u0wcaI50jQ+3iwvXBi6d4c+feDOO62N0d2pJy67Xb0KBQu69PANCaFIWBi+ISEub5Pmw9XkEVizZg233XYb+/btY+fOnTRs2JDNmzfTpEkTtm/fztmzZ6lWrRorVqxwbBMbG8vjjz9O8+bNmT9/PuXLl6dVq1acOXPG5ePGx8ezdu1aGjVq5FL9Xbt20bBhQypUqED58uV5/vnnuXLliuP9cePGUaVKFWrWrMm9996L8f8rGb/00ktUrlyZWrVq0a5dO0f9bt26MXPmTJfjFRGR7HXtGsydC/fcA/XqwYIFZgJ3990waxacPAkff6wEzhXqifNSDRo04PDhwxQqVMhR1rJlS2JiYggNDWXs2LGMHj3akRRFR0dz33338eijj/L2228DMHHiRCZNmkSzZs3Ys2cPPi70Am7bto177rnHpbpnzpyhVatWzJ07l2bNmpGYmMiAAQPo3bs3Cxcu5O+//+bzzz9nz549BAYGYhgGPj4+fP/99/z555/s378fX19fRxsAChYsSGhoKEePHqWc+uVFRHLN8ePm9CAzZsD582ZZUBA8/jg895yZ1EnGKInLbsHBEBPjUlW73U5UVBQhISHZM11HcHAGqqZf9/qYChUqxNKlS7njjjuc6jz//PM8//zzjtc1a9akaNGiqe7z0KFDVK5c2aUYJ02aRKdOnWjWrBkAfn5+fPjhh9xyyy0cOXIEwzAwDAO73Q7gSAztdjuJiYmO5O3GhPHOO+/k0KFDSuJERHKYYcCmTeYl0+XLzbuIAMLDzcStVy8oVszKCD2bkrjs5uMDBQq4Vtduh8REs34uzrk2evRo5syZk6w8LCyMSpUqJSvv3Lkzb731VrIELiUffvhhmu9funSJm266yaU49+3bR5cuXZzKgoKCqFWrFvv27aNNmzZ06dKFiIgIBgwYQK9evQgMDOTBBx/km2++oUaNGgwZMoTOnTs7JaRFixblYtIskSIiku1iYsxlsCZOhAMH/it/8EHo189cgD6vLoWVm/QReqHBgwczePBgEhMTMzwoAaB79+788ssvKb537tw5du3aRXh4eIrvFy1alMuXL7t0nNRiMwzD8d7LL7/ME088wWuvvUb9+vXZtGkT+fPn54MPPuDvv/9m0KBBLF68mOXLlzt65C5dukRoaKhLMYiIiOv+/BMmTYI5c/5bx7RAAXjySXj+eahSxdr48hoNbPBiXbt2ZcOGDRnebu7cuezfvz/FR4UKFdKceqRixYr8/vvvLh2nZs2a/PDDD05lcXFx7N27l+rVqzvKSpYsycyZMylUqBCrV692lJcvX55ly5Zx6NAhp6Tz4MGDVKxY0dXmiohIGux2+OWXEjzyiB933AEffWQmcLffbj7/5x+YPFkJXE5QT5wXS0hIICEhIcPb9enTh3Xr1qV4X12RIkXSvNfsvvvu4/nnn3cabJCaZ555hmrVqtGiRQuaN29OQkICL7zwAo0bNyYsLIyrV6/i7+9PYGAgkZGRnDhxgrJlyxIVFUWBAgXw8/Pj1KlTXLp0iVL/P6V3TEwMFy5c4NZbb81wu0VE5D/R0eYaphMm+PPnn/UA846iFi2gf39o2tS71jG1gpI4L+bKCNGUHDp0iCVLlnD33XdneNugoCCaNWvG+vXreeyxxwDzsmmbNm0IDAx01Js6dSp169ZlzZo1vPjii/Tv3x+73U6bNm0c991t376dHj16ULBgQex2O88++yz16tVj/vz5DBs2jJCQEHx8fBg/fjxhYWEAzJs3j169emWq3SIiAkePmve6zZyZdMnUh+BgG717+9Kvnx+3325xgF5ESZwXq1KlCs8++6zTNCPX8/X15ccff6RgwYJO5ZUqVaJjx46pjnAdOHAgvXv3TvW4r776Kr1793YkcX/99VeqdatXr87G61c8vk6jRo1SXK6rW7dudOvWLVm5zWZj+fLlLFu2LNXjiYhIcoYBP/4I48fDsmX/jTKtWBH69UukePG1tGvXjIAArYWVm5TEebG33nqLt956K8PbTZ06NUvHLVWqFO3bt+err75yJHK5Ydq0abz++usuTa8iIiLmJLxLl5rJ265d/5U3aQKDBkGzZpCYaOebbxKtC9KLKYkTS7Rr146QkJBcPWa/fv1y9XgiIp7qwgWYNs0caXrqlFkWFARPPAEDB0LVqv/VTVT+ZhklcSIiIgLA77+bS1599pm5PBZAqVLm9CDPPgvFi1sbnzhTEpcNXBlpKZ5J51ZE8jrDgLVrzelA1q79r7xmTfOSaceOcN24M3EjSuKyICAgAICrV6+SP39+i6ORnHD16lXgv3MtIpJXxMfDwoXwwQewf79Z5uMDbduaydv995uvxX15ZBI3Y8YMJkyYgK+vL2XKlGHmzJmULVs2xbqNGzfm6NGjTiMs27Vrx/Dhw7Mch5+fH0WKFHFMbhscHJyhaTvsdjvx8fFcu3Yte9ZO9RCe0G7DMLh69Spnz56lSJEimVrZQkTEHV2+DNOnm5dNk+53K1jQXMe0f38oX97S8CQDPC6JW7t2LdOnT2fr1q0ULlyYJUuW0K5dO7Zv355i/YSEBKZOnUrjxo1zJJ6kSWTTWqUgNYZhEBsbS/78+TM9Z5sn8qR2FylSxHGORUQ82fHj5iXTGTPMtU0BSpc2Byo8+ywUKWJldJIZHpfETZs2jREjRlC4cGEAOnbsyEcffcTevXuJiIjI9Xh8fHwoXbo0JUqUwGazZWhbm83Gli1baNCggVddrvOUdgcEBKgHTkQ83p49MG4cLF7830jSKlXg5ZehSxfd7+bJPC6J27hxI/PmzXMqa9iwIevXr8+WJC4uLo64uDjH66ioKMBMPNJL0jL6B99ut5OQkICfn59XJQue0m673Y49aUbLbJD085PRZN/TeWu7wXvbrnZb327DgPXrffjwQ1++++6/21YaNbLz4ot2mjY1HPe7ZTVcd2p3bsuptru6P49K4mJiYvD396dAgQJO5eHh4ezbty9bjvH+++/z9ttvJytPba3Q7LB+/foc2a+7U7u9i7e2G7y37Wp37rPZfNi6NYzly2/j2DHzipWvr5377jtF27Z/ctttkSQmwrffZv+xvfV8Q/a3PWlQXXp8DA+aQ+Gff/6hTp06nDx50ql89uzZbN68mblz5ybbplGjRly5coWEhAQSExNp1KgRw4cPJzQ0NMVjpNQTFx4ezvnz57N9clqbzcb69etp0qSJW19WzG5qt9rtLWw2Gw888ACbNm3yqrZ76zm3st3R0TBjhi+ffOLLyZNmF1uBAgY9e9rp39/OLbfk3LG99XxDzrU9KiqKYsWKERkZmWbu4VE9cUFBQVxLmn3wOkk3yadkyZIlhIaG4ufnR1RUFMOGDaNz586sW7cu1WMEBQUlKw8ICMixH86c3Lc7U7u9i7e2G7y37Wp3zjt3DiZMMBekv3zZLCtdGgYMgGef9aFoUT8gd25b8dbzDdnfdlf35VFJXLFixYiNjSUmJsZpypATJ04QFhaW4jbFr5teOiQkhI8++ohChQoRGRnpGBwhIiLiSY4fNwcrzJwJsbFmWcWK8Oqr0LWruUSW5H3uOUlXKnx8fKhTpw5btmxxKt+8eTP33nuvS/uw2+34+vq69Q31IiIiKTl4EHr0gNtug08+MRO4u++GL780l8x6+mklcN7Eo5I4gAEDBjB8+HDHqNElS5Zw5coVHnjggRTrHzt2zPE8KiqKvn370rp1a6eePBEREXe2cye0a2dODTJ3LiQkwEMPwfr1sGOH+Z6bzp0uOcijLqcCPProo5w4cYJ69erh6+tLqVKlWLFiBb6+vthsNjp27MiUKVMcE7T269ePv/76i8D/nwinXbt2vPLKK1Y2QUREJF2GARs3wqhR5r9JHn0UhgyB2rWti03cg8clcWD2xg0YMCBZeUBAAMuWLXMq+/rrr3MrLBERkSyz22H5cnj/fdi1yyzz9zfvdRs8GO6809LwxI14ZBInIiKS19hs5oL0o0bBoUNmWf780Ls3vPQS3HyztfGJ+1ESJyIiYqH4ePM+t/ffhyNHzLIiRaBfP3OqkOsmWRBxoiRORETEAteuwezZZs/biRNmWYkSZq9bnz6QzfPLSx6kJE5ERCQXXb0K06fD2LFw6pRZVrq0OcfbM89ADq3wKHmQkjgREZFcEBMDU6aYk/SePWuWhYebgxV69oR8+ayNTzyPkjgREZEcFBlpLos1fjxcuGCW3XorDB0K3bvD/8+AJZJhSuJERERywKVL8PHH5iNpXdMKFeC116BLF/DSZUYlGymJExERyUaXLplLYn38MURHm2V33gmvvw6dOoFWfZTsoiROREQkG0RGwqJFFXnySX/+f2VI7rrLTN7at9eyWJL9lMSJiIhkQXQ0TJgA48b5c/lyJQCqVYO33oK2bZW8Sc5REiciIpIJMTHmgIWxY+HiRQAfwsOjGD06mE6d/JW8SY5TEiciIpIBV6/C5MkwejScP2+WVawIr72WQIEC39O6dQslcJIr9GMmIiLigthY+OgjKF8eXnnFTOBuvx3mzYMDB6BzZ0ODFiRXqSdOREQkDdeuwYwZ5tqm//5rlt16KwwfDt26gf///yW1262LUbyTkjgREZEUJCTAp5/C22/DP/+YZTffDG+8YU7Sq3nexGpK4kRERK5jt8MXX5jJ2h9/mGVly5pThTz9tFZYEPehJE5ERAQwDFizxlxRYc8es6xYMRg2DPr21dqm4n6UxImIiNfbts1cy/SHH8zXhQrByy/DoEHmcxF3pCRORES81t69Zs/bN9+Yr4OCoF8/GDLE7IUTcWdK4kRExOv88Yc5unTRIvO1n595v9vw4RAWZm1sIq5SEiciIl7j5EkYMQJmzYLERLOsc2dzBOodd1gbm0hGKYkTEZE87/Jlc563CRPMed8AWrSAkSMhIsLKyEQyT0mciIjkWXFx5hJZ776btL4p1K8P770H999vbWwiWaUkTkRE8hy7HRYvNqcHOXrULKtSBUaNgpYtwcfH0vBEsoWSOBERyVO+/95c2/SXX8zXZcqY98H16IHWNpU8RUmciIjkCfv2mVODJE0XUqgQDB4ML7wABQpYGppIjlASJyIiHu2ff8ypQT791Fx1wd8f+vQxl80qUcLq6ERyjpI4ERHxSJGRMGYMjB8PsbFmWYcO5ojTChWsjU0kNyiJExERj5KQADNmmL1v58+bZfffD2PHQt261sYmkpuUxImIiMdYuxZeegkOHDBfV6oEo0dD69YacSrex9fqAERERNJz8KA5NUjz5mYCFxoKn3wCv/0GbdoogRPvpJ44ERFxWxcuwFtvwZQp5jJZ/v7Qv785aKFoUaujE7GWkjgREXE78fEwaZI5v9vly2ZZmzbmfW9a41TEpCRORETchmHAypXmZL1//GGW3XWXOQL1wQetjU3E3eieOBERcQu//gqNG0PbtmYCV7KkOQp1924lcCIpUU+ciIhY6sIFeP11mD7dXPM0KAhefBGGDjVXXRCRlCmJExERSyQmwrRpZgJ36ZJZ1rGjOWVIuXKWhibiEZTEiYhIrvvhB3OU6a+/mq+rVTOnDGnY0Nq4RDyJ7okTEZFcc/IkdOkCDRqYCVyRIjBxonnfmxI4kYxRT5yIiOS4uDhzhOm778KVK+bkvL17m+ucFitmdXQinklJnIiI5KhvvoEXXvhvypB77zUvndasaWlYIh5Pl1NFRCRH/PkntGplLpf1xx9QqhR89hls3aoETiQ7qCdORESy1dWrsGBBJVas8Cc+HgICzJ64N97QlCEi2UlJnIiIZJvVq6FfP3+OHq0IQLNm8PHHULGixYGJ5EFK4kREJMuOH4eBA2H5cgAfihW7yuTJgTz2mD8+PhYHJ5JHKYkTEZFMs9nMUadvv21eRvX3h4EDE7n77u9o27aZEjiRHKSBDSIikilbtkBEBAwebCZw9evDnj3w/vt28udPtDo8kTxPSZyIiGTI2bPQvbs5Oe/vv5vzvH36KWzeDFWrWh2diPdQEiciIi5JTISpU81BCp99Zk7Y++yzcPiwmdTp0qlI7tI9cSIikq59+8wVFrZvN19HRJgJXZ06loYl4tXUEyciIqmKjYVhw8zJebdvN+d5+/hj2LlTCZyI1dQTJyIiKfr+e3jmGXPlBYBHHzWXyypb1tq4RMSknjgREXFy8SI8/TQ8+KCZwJUpA199ZT6UwIm4DyVxIiICgGHA55/DnXfCnDnmQIXnnjNHoD76qNXRiciNdDlVREQ4etRM2L791nxdpQpMnw733mtpWCKSBvXEiYh4sYQEc8WFKlXMBC4wEN55B3bvVgIn4u7UEyci4qX27TPvfdu1y3zdoIHZ+6bF6kU8g3riRES8jM0GI0ZArVpmAlekCMyYYY5GVQIn4jnUEyci4kX27IGnnoJffzVfP/IITJkCpUtbG5eIZJx64kREvEBcHLz+Otxzj5nA3XSTORJ12TIlcCKeSj1xIiJ53I4d5r1vBw6Yrzt0gIkToUQJa+MSkaxRT5yISB4VGwuvvgr16pkJXIkSsHQpLFmiBE4kL1BPnIhIHvTjj2bv2+HD5uuuXc01T2+6ydq4RCT7qCdORCQPiY2FQYPg/vvNBK50aVixAubPVwInkteoJ05EJI/YsQOefPK/3rennoIPPzSnEBGRvEc9cSIiHi4+HoYPN1dYSOp9++YbmD1bCZxIXqaeOBERD7Z/v9n7tmeP+frxx82Rp6Gh1sYlIjlPPXEiIh4oMRHGjjVXXdizx7zfbckSWLhQCZyIt1BPnIiIh/n7b+jeHbZuNV+3amUum1WqlLVxiUjuUk+ciIiHMAyYNg3uustM4AoWhJkzYeVKJXAi3kg9cSIiHuD0aXPet2+/NV83bAhz5sCtt1obl4hYRz1xIiJu7uuvoVo1M4ELCjKnDfnuOyVwIt5OPXEiIm7q6lV4+WWYMsV8fddd5sCFKlWsjUtE3IN64kRE3NDu3ebI06QE7qWXzMl8lcCJSBIlcSIibsRuN6cOqVsXDh0yJ+5dvx7GjTMvpYqIJNHlVBERN/HPP+bUId99Z75+9FFz6hCteSoiKVFPnIiIG/jiC/Oet+++g+Bgc+qQL79UAiciqVNPnIiIha5cgQEDzHVOAe6+GxYsgDvusDYuEXF/6okTEbHIb7+ZSdvs2eDjA8OGwY8/KoETEdeoJ05EJJclrbzwwgsQFwdlypi9bw88YHVkIuJJlMSJiOSiy5ehd29YutR83aIFfPopFC9uZVQi4ol0OVVEJJds3w41apgJnL+/OW3I118rgRORzPHYJG7GjBlUq1aN6tWr8/DDD3Py5MlU60ZHR9OtWzeqVq1KlSpVGDFiBIZh5GK0IuLNkuZ+u/9+OHrUXC5r2zZzAl9fj/0tLCJW88hfH2vXrmX69Ols3bqVX3/9laeeeop27dqlWv+ZZ56hcuXK7N+/nz179rB7926mJE2DLiKSg2w2X9q29ePVVyEhATp0gD17oHZtqyMTEU/nkUnctGnTGDFiBIULFwagY8eO+Pn5sXfv3mR1L168yLZt2xg8eDAAgYGBjBkzhunTp+dmyCLihTZt8uHAgWKsWeNLvnzmYIbFi+H/f3WJiGSJRw5s2LhxI/PmzXMqa9iwIevXryciIsKpfNOmTdStWxc/Pz9H2R133MHZs2c5e/YsJUqUcKofFxdHXFyc43VUVBQANpsNm82Wre1I2l9279fdqd1qtze4tvt3nulSHpvNl0qV7CxYkEi1amZvXF7nredc7faudkPOtd3V/fkYHnZzWExMDLfccgsXLlxwKp88eTL79u1Ldpl04sSJHDlyhA8++MCpvHbt2nzyySfUqVPHqfytt97i7bffTnbcmjVrOiWCIiKp8bHbuWn/fowEg80+wVSufge+vh71q1ZELJSYmMju3buJjIwkJCQk1Xoe1xN3+fJl8uXLl6w8X758XL16Ncv1hw4dyosvvuh4HRUVRXh4OOvWrUvzg8wMm83G+vXradKkCQEBAdm6b3emdqvdeZ3v++/j98sv2EuVol6ZMny/5XuvaTt45zkHtdvb2g051/aoqCiKFSuWbj2PS+KCgoK4du1asvLY2Fjy58+fYv1Lly5lqH5QUFCy8oCAgBz74czJfbsztdu7eE27jx2DUaMAsI8ejTFxove0/QZqt3fx1nZD9rfd1X153MCGYsWKERsbS0xMjFP5iRMnCAsLS1Y/LCyM48ePJytPrb6ISJYMGgSxsdCwIUbnzlZHIyJ5mMclcT4+PtSpU4ctW7Y4lW/evJl77703Wf169eqxbds2EhMTHWWHDx8mMDBQSZyIZK+1a2HZMvDzg4kTzQVRRURyiMclcQADBgxg+PDhjpGjS5Ys4cqVKzyQwsKD5cqV45577mH06NGAef168ODB9O/fPzdDFpG8Li4Okn6v9O8PVataG4+I5Hked08cwKOPPsqJEyeoV68evr6+lCpVihUrVuDr64vNZqNjx45MmTKFUqVKATBnzhz69u1LlSpVsNvtPPLII7z00ksWt0JE8pQPP4Q//oCSJeGtt6yORkS8gEcmcWD2xg0YMCBZeUBAAMuWLXMqCw0NZfHixbkVmoh4m+PH4d13zefjxmk2XxHJFR55OVVExK289BJcvQr160PXrlZHIyJeQkmciEhWrF8PS5dqMIOI5DolcSIimRUf/99ghuefh7vusjYeEfEqSuJERDJr3Dg4fBhKlIAUlusTEclJSuJERDLj77/hnXfM5x98AEWKWBqOiHgfJXEiIhllGNCvH1y7Bo0aaTCDiFhCSZyISEZ99RV8+y0EBsKUKRrMICKWUBInIpIR0dEwcKD5/NVXoWJFa+MREa+lJE5EJCPefBNOnoTy5WHYMKujEREvpiRORMRVe/fChAnm80mTIH9+S8MREe+mJE5ExBV2O/TtC4mJ0KEDNG9udUQi4uWUxImIuGLmTPj5ZyhUCMaPtzoaERElcSIi6Tp7FgYPNp+/+y6ULWttPCIiKIkTEUnfyy/D5ctQowY895zV0YiIAEriRETS9v33MG+eORfc1Kng7291RCIigJI4EZHUxcf/1/PWty/Urm1tPCIi11ESJyKSmnHj4NAhKFkSRo60OhoRESdK4kREUvLHHzBihPlcC9yLiBtSEiciciPDgGefhbg4aNIEunSxOiIRkWSUxImI3GjOHHNAQ/785mAGLXAvIm5ISZyIyPXOnDGnFAHzcmr58tbGIyKSCiVxIiLXe+EFuHQJatY0n4uIuCklcSIiSb75BhYtAj8/mDFDc8KJiFtTEiciAhATY84FBzBokNkTJyLixpTEiYgAvP46HD8O5crBW29ZHY2ISLqUxImI7NgBEyaYz6dNgwIFrI1HRMQFSuJExLvZbNCrlzk3XLdu0LSp1RGJiLhESZyIeLdx42DfPrjpJvjwQ6ujERFxmZI4EfFef/wBb79tPh8/HooXtzYeEZEMUBInIt7pxqW1unWzOiIRkQxREici3klLa4mIh1MSJyLe5+RJePFF87mW1hIRD6UkTkS8i2FAnz4QGQm1a5sT+4qIeCAlcSLiXT7/HFatgoAAmD3bXGJLRMQDKYkTEe9x5gz0728+Hz4cqlSxNh4RkSxQEici3qN/f7h4ESIiYPBgq6MREckSJXEi4h2+/BK++MK8fDp7tnk5VUTEgymJE5G878IFeP558/mQIVCjhrXxiIhkA/+MbmCz2fjqq6/49ttvOXz4MJcuXSI0NJSKFSvSrFkz2rVrR2BgYE7EKiKSOS+8YN4Pd+ed8MYbVkcjIpItMtQTN23aNO6//37279/PU089xYoVK9i3bx/Lly+nR48e7N+/n/r16zNt2rScildEJGNWrYL588HX15zgNyjI6ohERLKFyz1xzzzzDA888AA//vgjfjcMyS9RogQlSpSgYcOGvP322yxYsIBevXoxc+bMbA9YRMRlly+bS2uBOblvnTqWhiMikp1cTuImTJhAvnz50q3n5+fHk08+SYcOHbIUmIhIlr3yCpw6BRUqmCsziIjkIS4ncTcmcP/73/9YvHgxV65cYdSoUY5ywzDw8fEhf/782ReliEhGrV8PSVcDZs0y10gVEclDMjU6ddGiRbRp04aAgABWrFjhKD9+/DjNmzfPtuBERDIlOhp69zaf9+sH9etbG4+ISA7I8OhUgPfee48tW7ZQokQJFi9e7Ci/+eabOXfuXLYFJyKSKS+/DMeOwS23wPvvWx2NiEiOyFRPXEJCAiVKlADAx8fH6b24uLisRyUikllr18L06ebzOXOgYEFr4xERySGZSuJCQ0P59ddfk5UvX77ckdyJiOS6y5ehZ0/zef/+0KiRpeGIiOSkTF1OnThxIp07d6ZNmzacP3+ecePGsWPHDtavX8/333+f3TGKiLhm4EA4edIcjXrdgCsRkbwoUz1xERER/PLLL1SoUIHHHnuMv/76i7p167J//34iIiKyOUQREResXAmffWZO6vvppxAcbHVEIiI5KlM9cQDBwcH06tUrO2MREcmc8+fhmWfM5y+9BPfea208IiK5IFNJ3KhRo4iPj0/xvcDAQIYMGZKloEREMuT55821UStX1qS+IuI1Mj061WazOR6RkZFs2rSJGTNmuLSqg4hItlmyxHz4+ZmXU/U7SES8RKZ64l5//fUUy5cvX+40+a+ISI46fRqee858PmwY1KplbTwiIrkoUz1xqWnbti379u3Lzl2KiKTMMMzF7S9cgIgISOU/lyIieVW2JnEXL17kypUr2blLEZGUzZtnjkgNCDAvowYGWh2RiEiuytTl1HfffTfZwIbz58+zatUq+vbtmy2BiYik6p9/YMAA8/lbb0G1apaGIyJihUwlcX5+fvj5+TmVVaxYkSeffJK6detmS2AiIimy281VGSIjoXZtePVVqyMSEbFEppK4oUOHZnccIiKumTwZ1q0zR6HOnQv+mZ7uUkTEo7n82+/EiRPYbLZ06wUEBBAeHp6loEREUnTwILzyivl87FioVMnaeERELORyEte4cWNsNhuGYaRZLygoiEOHDmU5MBERJ/Hx0K0bXLsGTZv+N7WIiIiXcjmJO3z4cE7GISKSthEjYPduCA2FOXPMNVJFRLyYfguKiPv78Ud4/33z+bRpUKaMtfGIiLiBTN0RfOrUKQYPHsyePXu4evWq03u+vr78+eef2RKciAjR0fDEE+ao1CeegMceszoiERG3kKmeuF69elG5cmUWL15MUFAQq1atYtSoUdxyyy3MnTs3u2MUEW82aBD8/Tfccgt88onV0YiIuI1M9cSdPHnSMc2Ir68vlStXpnLlytSvX58OHTqwdevWbA1SRLzU8uUwaxb4+JjTiRQubHVEIiJuI1M9cYmJiY7nJUqUcIxGLV26tJbdEpHsceYM9O5tPn/5ZWjY0Np4RETcTKaSuLCwMEfi9vDDDzN8+HAuX77M999/T6DWLxSRrDIMc1WG8+ehenV45x2rIxIRcTuZupw6depUx/OBAwfSq1cvwsPDKVOmDAsWLMi24ETES82YAatXm4vaz58PQUFWRyQi4nZcTuLuueceOnXqRJcuXShXrpyjPCgoiHnz5uVEbCLijf74wxzMAOa0IlWrWhuPiIibcvly6syZMzl79iz33nsvDz30EHPmzCE6OjonYxMRbxMfD126wNWr0KgRvPCC1RGJiLgtl5O46tWrM2bMGI4ePcqbb77Jjh07qFy5Mh07dmTFihUkJCTkZJwi4g3efBN27YKiReGzz7Qqg4hIGjL1G7JBgwZMmTKFI0eO0KNHD5YuXcptt91Gnz59+PHHH7M7RhHxBt99B6NHm89nzoSwMGvjERFxc1n6b66/vz8tWrRg3rx5bN++nX/++Yf69etnV2wi4i0uXDBXYzAMc1qRdu2sjkhExO1lKYmLiYlhwYIFPPLII9SpU4dy5cppol8RyRjDgF694NQpqFgRxo+3OiIREY+Q4SlGrl69ysqVK1m8eDE//fQTDz/8MM899xyNGzfGz88vJ2IUkbxs+nRzZYaAAPj8cyhQwOqIREQ8gstJ3LJly5g/fz6bNm2iUaNGdO3alUWLFhGk+ZtEJLMOHvxvOpFRo6BGDWvjERHxIC4ncTNmzKBjx47MmTOHkJCQnIxJRLxBXBw8/jjExkLTpppOREQkg1y+J65v37706NHD5QRu+fLlmY1JRLzB0KHw669QrBh8+qmmExERySCXf2teu3aNzp07s2HDhjTrffvtt7Rr1w6bzZbl4EQkj1qz5r8BDHPmQOnS1sYjIuKBXL6c2qFDBxo2bMiYMWMYNGgQtWvXpnLlyhQpUoRLly5x4MABduzYQfPmzZkyZQolS5bMybhFxFOdOQPdu5vP+/WDVq2sjUdExENlaHRqiRIlGDduHO+//z7btm3j0KFDnD17lqJFi/Lkk08ybdo0AgMDcypWEfF0hgFPPw1nz5proo4ZY3VEIiIeK8NTjAAEBATwwAMP8MADD2RzOOmbMWMGEyZMwNfXlzJlyjBz5kzKli2bav3GjRtz9OhRChYs6Chr164dw4cPz41wReR648fDN99AUJA5nUj+/FZHJCLisTKVxGWX8+fPU758eWrWrJlqnb1797J3717KlSvH2rVrmT59Olu3bqVw4cIsWbKEdu3asX379lS3T0hIYOrUqTRu3DgnmiAiLvLZuRMGDzZfjB9v9sSJiEimWZrEAbRp04bHH3+cli1bJntv7dq1LFy40PF62rRpjBgxgsKFCwPQsWNHPvroI/bu3UtERERuhSwiGeR/5Qp+gwZBQgK0bw99+lgdkoiIx7N8TH/jxo2JiYlh3759TuUHDx7k33//pXXr1o6yjRs30qBBA6d6DRs2ZP369bkSq4hkgmEQMWkSPkeOQLly5uL2Pj5WRyUi4vEs74kD6NSpE7NmzaJUqVIUL16cixcvsnHjRvr168fSpUsBc51Wf39/CtywJE94eHiyBDAr4uLiiIuLc7yOiooCwGazZfu0KUn787bpWNRu72q3MXUqZX/8EcPfn8QFCzAKFAAv+Qy89Zyr3Wq3t8iptru6P7dI4gB69uzJ9OnT6dq1K/Pnz6dfv35O71++fJl8+fIl2y5fvnxcvXo11f36+PgwbNgwXn31VRITE2nUqBHDhw8nNDQ0xfrvv/8+b7/9drLydevWERwcnMFWucZbexLV7rwv5OhRGrzyCgAHunXjr3PnzIENXsabzvn11G7v4q3thuxve1p5zfVcSuL+/fffDGWZ/v7+lClTxuX6STp37ky3bt2YMWMGvjfM3h4UFMS1a9eSbRMbG0v+NEa4LVmyhNDQUPz8/IiKimLYsGF07tyZdevWpVh/6NChvPjii47XUVFRhIeH07Rp02xfbsxms7F+/XqaNGlCQEBAtu7bnandXtLumBj8X30VH5uN07VqUf6TT6joZWst22w2Ro4c6T3n/P953c/6/1O7vavdkHNtT7oKmB6XkrhWrVphs9kwDMOlnQYGBvLLL7+4VDeJYRjMnTuXefPmMXv2bPr16+eUyBUrVozY2FhiYmKcpgs5ceIEYWFhqe63ePHijuchISF89NFHFCpUiMjISMcAiesFBQURlMIfmoCAgBz74czJfbsztTuPGzQI/vc/jDJl2DNwII2Dgryj3SnwmnN+A7Xbu3hruyH72+7qvlxK4jKakGXG1KlT6dSpEwULFqRbt25MmjSJ/v37O9738fGhTp06bNmyhRYtWjjKN2/ezMiRI10+jt1ux9fXFz8/v2yNX0Su89lnMHcu+PqSOG8e8dHRVkckIpLnZMvo1L1797Jjx45Mb79w4ULuv/9+SpQoAUBoaChNmzZlzpw5TvUGDBjA8OHDHd2MS5Ys4cqVK2lOOnzs2DHH86ioKPr27Uvr1q2devNEJBsdPgzPPWc+f/NNjPr1rY1HRCSPynASd+LECRITEx2vr127Rq9evVy+1HqjdevWUaRIEapVq+ZUXrFiRW6++Wa+/vprR9mjjz7Kk08+Sb169ahWrRozZsxgxYoVjsuuNpuNRx99lNOnTzu26devH5UrVyYiIoIGDRpwyy23JEsORSSbxMZCx45w5Qo0agSvvWZ1RCIieZbLo1M3bNhA48aNqVatGqGhobz55pt06dKFTp060aNHD+rUqZOpAFavXs2pU6cYk8oair/99pvTaNEBAwYwYMCAFOsGBASwbNkyp7Lrk0ARyWEvvQS//QbFi8OCBeDnB3a71VGJiORJLidxAwYM4PfffycsLIwvv/yS559/nqFDh9KvX79k04G4qlixYkRGRmZqWxFxM0uXwpQp5vN586B0aWvjERHJ4zJ8OdXHx4eKFSuyYcMGHn/8cXbt2pUTcYmIJ/nrL+jVy3w+eDA0a2ZtPCIiXsDlJC6le94++OADwsLCePnll7M1KBHxILGx8NhjEBkJ994L77xjdUQiIl7B5STujTfeSLF8/PjxbNu2jd27d2dbUCLiQQYOhL17oVgxWLwYvHSeKBGR3OZyEtelSxcAevfu7VTu5+fH+PHjeffdd7M3MhFxf599BjNmmAvaL1wIaUy8LSIi2SvD98SlNDK0bt26LFmyJFsCEhEPsX8/9OljPn/zTWjSxNp4RES8TKYn++3QoYPTa39/lwe6ioini44274OLjTWTt9dftzoiERGvk+kk7sCBA9kZh4h4CsOA3r3NlRnKlv1vPjgREclVLnWftWvXjoSEBKeyEydO0KZNmxTrBwQE8OWXX2Y9OhFxP5MnmwMY/P1hyRJzYl8REcl1LiVxL7/8MjabzanspZdeSrV+gEanieRNO3bAoEHm8zFjzClFRETEEi4lcffqF7WIXLgAHTqAzQbt2sELL1gdkYiIV8v0PXEi4kXsdnjySTh+HG67DWbPNqcVERERy7icxDVs2JDChQtTq1Yt3njjDY4dO5aTcYmIOxk9Gr75BoKCzDVSCxe2OiIREa/nchJ3/vx5Ll++zIIFCyhQoACNGjVi8ODByQY8iEge8/33/00hMnEiRERYGo6IiJhcTuL8/Pzw8fGhUqVKDBkyhAMHDhAfH0/Dhg2JiorKyRhFxConT8Ljj5uXU7t3h549rY5IRET+X6bvicufPz/jx4/niSeeoHnz5sTHx2dnXCJitbg4c0LfM2egWjVzahHdByci4jZcTuIMw0ixvE+fPjRs2DDNKUdExAMNGgQ//wxFisCyZRAcbHVEIiJyHZeTuPr166f63jvvvMN3333HiRMnsiUoEbHY3LkwZYr5fP58c0SqiIi4FZcXPJ08eXLqO/H3Z8eOHRQoUCBbghIRC+3e/d/C9m+9BS1bWhqOiIikLNvmiVMCJ5IHXLhgTuR77ZqZvL3xhtURiYhIKjTZr4iYEhOhSxc4dsy8fDpvHvjqV4SIiLvSb2gRMQ0fDuvWQf788NVXULSo1RGJiEgalMSJCCxfDu+9Zz6fORPuusvScEREJH1K4kS83eHD5rqoAAMHmpdURUTE7SmJE/Fm0dHmQIboaKhfH8aOtToiERFxkZI4EW9lGPD00/D771C6NCxZAgEBVkclIiIuUhIn4q0++ACWLjUTt6VLoVQpqyMSEZEMUBIn4o3WroXBg83n48fDvfdaG4+IiGSYkjgRb/Pnn9C5M9jt5uXU556zOiIREckEJXEi3iQ6Gh55BC5fhrp1YfJk8PGxOioREckEJXEi3sJuhyeeMAcylCljTugbFGR1VCIikklK4kS8xYgRsGIFBAaaCVzp0lZHJCIiWaAkTsQbfPUVvP22+Xz6dKhTx9p4REQky5TEieR1+/c7r8jQvbu18YiISLZQEieSl128aA5kuHIFHnwQxo2zOiIREckmSuJE8qqEBOjUCf7+G2691VyRwd/f6qhERCSbKIkTyasGD4YNGyA4GJYvh5tusjoiERHJRkriRPKiefPgww/N5599BnfdZW08IiKS7ZTEieQ1O3dC797m89dfh/btrY1HRERyhJI4kbzk5Elo2xbi4qB16/+mFRERkTxHSZxIXnH1qjkS9dQpqFIF5s8HX33FRUTyKv2GF8kL7Hbo0QN++QWKFYOVKyEkxOqoREQkBymJE8kLRoyAL76AgABzdYby5a2OSEREcpiSOBFPt3jxf/e+TZ0K9etbG4+IiOQKJXEinmznTvMyKsBLL8HTT1sajoiI5B4lcSKe6uRJcyDDtWvQsiWMHm11RCIikouUxIl4oqSRqP/+a45EXbgQ/PysjkpERHKRkjgRT3PjSNSvv9ZIVBERL6QkTsTT3DgS9dZbrY5IREQsoCROxJNoJKqIiPw/JXEinkIjUUVE5DpK4kQ8wbFj5lqoGokqIiL/T0mciLuLjIRWreDMGbjrLo1EFRERQEmciHuz2aBjR9i/H0qXhlWrNBJVREQAJXEi7sswoH9/WLcOgoPNqUTCw62OSkRE3ISSOBF39eGHMG0a+PiYl1Br1bI6IhERcSNK4kTc0bJl8Mor5vMPPjBXZxAREbmOkjgRd7NzJ3Ttal5Ofe45eOEFqyMSERE3pCROxJ0kTSUSGwsPPwwff2xeThUREbmBkjgRd3HjVCKLFoG/v9VRiYiIm1ISJ+IONJWIiIhkkJI4EatpKhEREckEJXEiVhs3TlOJiIhIhimJE7HS55/Dq6+azzWViIiIZICSOBGrbNoE3bubz194AQYNsjIaERHxMEriRKxw4AC0bWsOaHjsMbMXTkREJAOUxInktpMnzTngIiPhvvtg3jzw1VdRREQyRn85RHKR/9Wr+LdpAydOQMWKsGIF5MtndVgiIuKBlMSJ5Jb4eO4ZPRqfffugZEn49lu46SaroxIREQ+lJE4kNxgGfn36UOLXXzEKFIDVq+HWW62OSkREPJiSOJHcMHw4vvPnY/f1JfHzzzUXnIiIZJmSOJGcNn06vPsuAL/27YvRvLnFAYmISF6gJE4kJ61eDX37ApD42mscb9LE4oBERCSvUBInklN27TIXtbfboUcP7MOHWx2RiIjkIf5WByCSJ/39N7RsCVevQtOm5iVVERGRbKSeOJHsdvYsNGtm/hsRAUuXQkCA1VGJiEgeoyROJDtFRZmrMfz5J5QrB998A4UKWR2ViIjkQUriRLJLXBy0awe7d0Px4rBuHZQubXVUIiKSRymJE8kOiYnw5JOwcSMULGiuxlChgtVRiYhIHqYkTiSrDAMGDoQlS8x735Yt02S+IiKS45TEiWTVyJEwaRL4+MC8edC4sdURiYiIF1ASJ5IVM2bAG2+Yzz/+GDp1sjYeERHxGh6bxM2ePZugoCCOHj2abt3o6Gi6detG1apVqVKlCiNGjMAwjJwPUvK2ZcugTx/z+WuvQf/+1sYjIiJexfLJfs+fP0/58uWpWbNmqnX27t3L3r17KVeuHABvvPEGu3btomjRoiQkJKR7jGeeeYZq1aoxf/584uPj6dixI1OmTOG5557LrmaIt9myBR5/3FyNoVcveOcdqyMSEREvY3kSB9CmTRsef/xxWrZsmey9tWvXsnDhQsdru91O6dKlWbVqFbfddlu6+7548SLbtm1j/vz5AAQGBjJmzBg6duyoJE4y57ffoE0bc0qRRx6BKVPM++FERERykVtcTm3cuDExMTHs27fPqfzgwYP8+++/tG7d2lHm6+vLc889h5+fn0v73rRpE3Xr1nWqf8cdd3D27FnOnj2bPQ0Q73HkiLkaQ2Qk1K8Pn38O/m7xfyEREfEybvPXp1OnTsyaNYtSpUpRvHhxLl68yMaNG+nXrx9Lly7N9H5PnTpFeHh4svKwsDCOHDlCiRIlnMrj4uKIi4tzvI6KigLAZrNhs9kyHUdKkvaX3ft1dx7b7rNn8W/aFJ/TpzGqViXhyy/NBM7Fdnhsu7PIW9sN3tt2tVvt9hY51XZX9+c2SRxAz549mT59Ol27dmX+/Pn069cvy/u8fPky+fLlS1aeL18+rl69mqz8/fff5+23305Wvm7dOoKDg7McT0rWr1+fI/t1d57Ubv8rV7jvjTco8vffXC1enB9efJFrP/6YqX15Uruzk7e2G7y37Wq3d/HWdkP2tz2l/CQlbpXEAXTu3Jlu3boxY8YMfH2zfrU3KCiIS5cuJSuPjY0lf/78ycqHDh3Kiy++6HgdFRVFeHg4TZs2JSQkJMvxXM9ms7F+/XqaNGlCgBctkO5x7Y6Nxa9VK3z//hujeHECvvuOBytWzPBuPK7d2cRb2w1m20eOHOl1bffWc652e1e7IefannQVMD1ulcQZhsHcuXOZN28es2fPpl+/fllO5MLCwtixY0ey8hMnThAWFpasPCgoiKCgoGTlAQEBOfbDmZP7dmce0e74eHMU6g8/QEgIPmvXElC1apZ26RHtzgHe2m7w3rar3d7FW9sN2d92V/flFgMbkkydOpVOnTpRsGBBunXrxqRJk7K8z3r16rFt2zYSExMdZYcPHyYwMDDFJE7EITERuneHb76B/Plh1SqoUcPqqERERAA3SuIWLlzI/fff7xhoEBoaStOmTZkzZ06W9luuXDnuueceRo8eDZhdn4MHD6a/JmaVtBgGPP88LFpkDl748ktzNKqIiIibcIskbt26dRQpUoRq1ao5lVesWJGbb76Zr7/+OsXtAgMDk3U52mw2Hn30UU6fPu0omzNnDr/++itVqlThrrvuolKlSrz00kvZ3xDJO4YNg2nTzPnf5s+Hhx+2OiIREREnbnFP3OrVqzl16hRjxoxJ8f3ffvstxRGj//vf/5KVBQQEsGzZMqey0NBQFi9enD3BSt43ejSMGmU+nzZN66GKiIhbsjyJK1asGJGRkVaHIWKaNg2GDDGfjxkDvXtbG4+IiEgq3OJyqohbWLQI+vY1nw8dCq+8Ym08IiIiaVASJwLmCNQnnjAHNPTtCyNHWh2RiIhImpTEiWzZAu3bQ0ICdOkCEydqQXsREXF7SuLEu+3eDa1bw7Vr0KoVfPopZMNKISIiIjlNf63Eex04AE2bQlQUNGwIS5aAl842LiIinkdJnHin//0PHnoILlyAu++GlSvNVRlEREQ8hJI48T5Hj5oJ3JkzcNddsHYthIRYHZWIiEiGKIkT73LyJDz4IPzzD1SqBOvXQ2io1VGJiIhkmJI48R5nzpg9cEeOwG23wcaN8P9r9YqIiHgaJXHiHS5ehCZN4PBhCA83E7gyZayOSkREJNOUxEneFxkJzZrBvn1QujR89x3ccovVUYmIiGSJkjjJ22JioEUL2LULihWDDRvg9tutjkpERCTLlMRJ3hUbC488Aj/+CEWKmIMYKle2OioREZFsoSRO8qa4OHMpre++g0KFzGlEIiKsjkpERCTbKImTvCchAR5/HL791pzAd/VqqF3b6qhERESylZI4yVsSE+HJJ2HZMggKMldiqF/f6qhERESynZI4yTsSE6FHD/j8c/D3h6VLoXFjq6MSERHJEUriJG+w26FnT5g/H/z8YPFiaNXK6qhERERyjJI48Xx2O/TuDXPnmgncokXQrp3VUYmIiOQoJXHi2ex26NMHZs8GX19YsAAee8zqqERERHKckjjxXIYBzz8PM2aYCdy8edCpk9VRiYiI5AolceKZDAP694epU8HHBz79FLp0sToqERGRXKMkTjyPYcALL8CkSWYCN3s2PPGE1VGJiIjkKiVx4lkMA156CSZMMF/PnGlOKyIiIuJllMSJ5zAMGDwYxo83X0+bBk8/bW1MIiIiFlESJ57BMGDYMBg71nw9eTI884y1MYmIiFhISZy4P8OAN96AUaPM1xMnQt++1sYkIiJiMX+rAxBJk2HA66/De++Zrz/6yJxWRERExMspiRP3ZRgwZAiMGWO+/vBDGDjQ2phERETchJI4cU+GAa+8Ah98YL6eMMGcF05EREQAJXHijgwDBg2Cjz82X0+aBM89Z21MIiIibkZJnLgXw4ABA8zBC2CuyPDss9bGJCIi4oaUxIn7sNuhXz+YMsVciWH6dOjVy+qoRERE3JKSOHEPdrs5bcj06WYCN2sWPPWU1VGJiIi4LSVxYj273Zy4d9Ys8PU1F7PXWqgiIiJpUhIn1kpMNC+ZfvqpmcDNmwddulgdlYiIiNtTEifWSUyEnj3NxM3PDxYsgE6drI5KRETEIyiJE0v4JCbi99RTsGiRmcB9/jl06GB1WCIiIh5DSZzkvvh47h43Dt+ffgJ/f1i8GNq1szoqERERj6IkTnLXtWv4dexImZ9+wggMxOeLL6BNG6ujEhER8ThK4iT3XLkCjzyC78aNJAQGwrJl+LdoYXVUIiIiHsnX6gDES0RFQfPmsHEjRsGC/Dx8OEaTJlZHJSIi4rHUEyc57+JFM4HbuROKFCHx66+5cOGC1VGJiIh4NPXESc46exYaNTITuJtugu++w6hTx+qoREREPJ6SOMk5p05Bw4bw229QqhRs3gw1algdlYiISJ6gy6mSM44dg4cegr/+gvBw2LgRKlSwOioREZE8Q0mcZL8//4QHH4QTJ6B8eTOBK1fO6qhERETyFF1Olez1++/QoIGZwFWqBFu2KIETERHJAUriJPvs2WPeA/fvv3DXXeY9cGXLWh2ViIhInqQkTrLH9u3mJdTz5+Huu+H776FECaujEhERybOUxEnWbdxoDmK4fBnuuw82bIDQUKujEhERydOUxEnWLFsGLVqYS2o1aQJr10LhwlZHJSIikucpiZPM+/RTeOwxiI+H9u3h66+hQAGroxIREfEKSuIkcz7+GJ56Cux2ePppWLQIgoKsjkpERMRrKImTjDEMeOsteOEF8/WLL8LMmeCvKQdFRERyk/7yiuvsdhg0CCZMMF+/+y4MGwY+PtbGJSIi4oWUxIlrEhKgVy+YO9d8PXEiPP+8tTGJiIh4MSVxkr5r1+Dxx2H5cvDzMwc0dOtmdVQiIiJeTUmcpC0mBtq2NeeCCwqCJUugTRuroxIREfF6SuIkdRcvmnPAbd8OBQvCypXQqJHVUYmIiAhK4iQ1p05B06Zw4IC5+sKaNXDPPVZHJSIiIv9PSZwk9+efZgJ35AiUKQPr1kGVKlZHJSIiItfRPHHibPduuPdeM4G77TbYulUJnIiIiBtSEif/2bgRGjaEc+egRg3Ytg1uvdXqqERERCQFSuLEtGSJOYghJgYefBA2bYKSJa2OSkRERFKhJE5g0iTo3NlcyP6xx+CbbyAkxOqoREREJA1K4ryZYcDw4dCvn/n8uee0kL2IiIiH0OhUb5WYaCZt06ebr99+G954Q+ugioiIeAglcd7o2jXo2hW++gp8fWHyZHj2WaujEhERkQxQEudtIiPhkUdg82YIDITPP4d27ayOSkRERDJISZw3+fdfePhh+PVXc+DCihXwwANWRyUiIiKZoCTOW1y/CkPJkuYyWhERVkclIiIimaTRqd5g1y64777/VmHYtk0JnIiIiIdTEpfXffONuQrD2bP/rcJw221WRyUiIiJZpCQuL5s1C9q0gatXoUkTrcIgIiKShyiJy4sMA956C3r1MueDe/JJWL1aqzCIiIjkIUri8hqbzUze3n7bfP3aa/DppxAQYGlYIiIikr00OjUviYmBDh3MkaeaxFdERCRPUxKXV5w5Ay1bwi+/QP78sHgxtG5tdVQiIiKSQ5TE5QWHD5uT+B45AsWKwapVUKeO1VGJiIhIDtI9cZ7up5+c54D78UclcCIiIl7AI5O42bNnExQUxNGjR9Ot27hxY26//XYiIiIcjxEjRuR8kLlh+XJ48EG4cAHuucdM4CpUsDoqERERyQWWXk49f/485cuXp2bNmqnW2bt3L3v37qVcuXIAvPHGG+zatYuiRYuSkJCQ7jESEhKYOnUqjRs3zq6w3cPkydC/P9jt5r1wixdDgQJWRyUiIiK5xPJ74tq0acPjjz9Oy5Ytk723du1aFi5c6Hhtt9spXbo0q1at4jZvXXXAboehQ2HMGPN1795mQudv+akUERGRXGT55dTGjRsTExPDvn37nMoPHjzIv//+S+vrRlj6+vry3HPP4efnl9thuofYWOjc+b8EbsQImDZNCZyIiIgXcou//p06dWLWrFmUKlWK4sWLc/HiRTZu3Ei/fv1YunRprsYSFxdHXFyc43VUVBQANpsNm82WrcdK2p9L+z13Dr/27fH9+WeMgAASp03D6NYNXLik7G4y1O48RO32rnaD97Zd7Va7vUVOtd3V/fkYhmFk65Ez4Pz586xatYoePXoAMH36dLp27cqsWbPo168fvr6+LF26lLvvvttxT1yScuXKsWHDBm6//fY0j9GoUSOuXLlCQkICiYmJNGrUiOHDhxMaGppi/bfeeou3k1Y7uM7ChQsJDg7OVDuzquDJk9QdMYICZ84QX6AAO4YM4UK1apbEIiIZM3LkSF577TWrwxARD3L16lW6dOlCZGQkIWksmelWSVxUVBTdu3dnxowZFCtWDCDLSdy5c+cIDQ3Fz8+PqKgohg0bxv/+9z/WrVuXYv2UeuLCw8M5f/58mh9kZthsNtavX0+TJk0ISGVZLJ8ffsDvscfwuXQJ49ZbSVixAipVytY4cpsr7c6L1G7vajeYbX/ggQfYtGmTV7XdW8+52u1d7Yaca3tUVBTFihVLN4lzi8upAIZhMHfuXObNm8fs2bMdPXFZVbx4ccfzkJAQPvroIwoVKkRkZCSFCxdOVj8oKIigoKBk5QEBATn2w5nqvhcsgKefhvh4qFsXnxUrCChRIkdisEJOfqbuTO32Pt7adrXbu3hruyH72+7qviwf2JBk6tSpdOrUiYIFC9KtWzcmTZqUI8ex2+34+vq69+AIw4B33oFu3cwErn17+O47yEMJnIiIiGSNWyRxCxcu5P7776fE/ycpoaGhNG3alDlz5mR538eOHXM8j4qKom/fvrRu3ZqCBQtmed85Ij4ennoKhg83X7/yCixZYq6HKiIiIvL/LE/i1q1bR5EiRah2w436FStW5Oabb+brr79OcbvAwMBk3Y02m41HH32U06dPO8r69etH5cqViYiIoEGDBtxyyy3ZkhzmiEuXoHlzmDsX/Pxg6lRzOpFsuKwsIiIieYvl98StXr2aU6dOMSZp7rMb/PbbbymOFv3f//6XrCwgIIBly5Y5laWWBLqdI0egRQs4dAgKFoQvvjATOhEREZEUWJrEJY288HY+O3ZAu3Zw9iyULQurV0P16laHJSIiIm5M1+ksVuj4cfwaNzYTuIgI2L5dCZyIiIiky/LLqd4uOjwco21bfKKiYNEiKFTI6pBERETEAyiJs5qPD4kzZuCbL5/WQBURERGX6XKqOwgKUgInIiIiGaIkTkRERMQDKYkTERER8UBK4kREREQ8kJI4EREREQ+kJE5ERETEAymJExEREfFASuJEREREPJCSOBEREREPpCRORERExAMpiRMRERHxQEriRERERDyQkjgRERERD6QkTkRERMQDKYkTERER8UBK4kREREQ8kJI4EREREQ+kJE5ERETEA/lbHYC7MwwDgKioqGzft81m4+rVq0RFRREQEJDt+3dXarfa7S1sNhuJiYle13ZvPedqt3e1G3Ku7Uk5R1IOkholcemIjo4GIDw83OJIRMRTFStWzOoQRMQDRUdHU7hw4VTf9zHSS/O8nN1u59SpUxQqVAgfH59s3XdUVBTh4eGcOHGCkJCQbN23O1O71W5v4a1tV7vVbm+RU203DIPo6GjKlCmDr2/qd76pJy4dvr6+hIWF5egxQkJCvO4HH9Rub+Ot7Qbvbbva7V28td2QM21PqwcuiQY2iIiIiHggJXEiIiIiHkhJnIWCgoJ48803CQoKsjqUXKV2q93ewlvbrnar3d7C6rZrYIOIiIiIB1JPnIiIiIgHUhInIiIi4oGUxImIiIh4ICVxuWT27NkEBQVx9OjRdOtGR0fTrVs3qlatSpUqVRgxYkSypTdmzJhBtWrVqF69Og8//DAnT57MocizJiNxfvPNN0RERDg9qlatSsmSJR115s+fT2hoqFOdOnXqkJiYmBvNcVlGz0/jxo25/fbbndo1YsQIpzorVqygRo0aRERE0KBBAw4cOJCTTciUjLTbMAyGDh1KzZo1qV69OhERESxatMipjjue74y00Ru/y556XlOTkfOTV77HSVxte1763Z3E1b/Zln/HDcmQc+fOGYUKFTIaNmyY6qNw4cLGkSNHHNu8/vrrRvPmzY2SJUsaf/zxR7rH6Ny5szFy5EjDMAwjLi7OeOSRR4xJkyY53l+zZo1x9913G5cvXzYMwzAWL15s1K5dO3sbmoKMtj074ly0aJHRuXNnx+s5c+YYXbt2zbY2uSI32t2wYUNj/fr1qb6/f/9+4/bbbzdOnjxpGIZh/PDDD8btt99uXL16NXsamYLcaPeXX35pxMXFGYZhGEeOHDHKlClj7N271/G+Fec7LRlto7t+lzMqo3F62nlNTUbb7Y7f48zK6s+mO/zuzqyM/M22+juuJC6Dzp07Z3Tt2tVYtWpViu+vWbPGePLJJx1/2BITE41JkyYZCQkJxi233JLuD8SFCxeM8PBwIyEhwVF2+PBho3r16o7Xjz76qPHNN984bVevXj1jz549mWqTqzLa9uyI84EHHjA2btzoeG1VEpfT7U7vl/+gQYOMyZMnO5U9/vjjxrJly1xqQ2ZYcb4HDhxofPDBB47X7vaLPyNtdOfvckZlNU53P6+pyWi73fF7nFlZPefu8Ls7MzLyN9sdvuO6nJoJjRs3JiYmhn379jmVHzx4kH///ZfWrVs7ynx9fXnuuefw8/Nzad+bNm2ibt26TvXvuOMOzp49y9mzZwHYuHEjDRo0cNquYcOGrF+/PrNNcllG2p7VOA8fPsw///xDo0aNsh54FuVmu1OyYcMGGjZsmK37dEVut/vixYvky5cva0HnoIy00d2/yxmR1Tjd/bymJrvPj1Xf48zIStvd6Xd3RmXkb7Y7fMeVxGVSp06d2LFjB+fOnQPMX1IbN26kR48eWdrvqVOnCA8PT1YeFhbGkSNHiImJwd/fnwIFCji9Hx4ezt9//52lY7vKlbZnR5zTp0+nZ8+e+Pj4ZFvsWZFb7U5JSj8XuXXOc6vd586dY82aNbRv3z7bYs9OGW2jJ3yXXZHVON39vKYmJ86Pld/jjMhq293td3dOcYfvuJK4LOjZsyfLli3jypUrzJ8/n+eeey7L+7x8+XKK/2PNly8fV69eTff93JJe27Ma57Vr11i4cGGypNjHx4ctW7Zw//33c+edd9K6dWt++umnLLUlI3Kq3T4+PgwbNsxxM/gLL7zAxYsX09xvbp7znD7fAAMHDqRv375ON0Nbfb6vl9E2esp3OT1ZjdPdz2tqMtNud/8euyor59xdf3fnBHf4jiuJy6LOnTvTrVs3unTpgq9v1j/OoKAgrl27lqw8NjaW/Pnzp/t+bkqr7VmNc+nSpdx7772UKlXKqfyxxx5j//79bN26ld9//50+ffrQpk0b/vjjj6w1JgNyot1Llizhp59+Yvfu3fzwww8kJCTQuXPnNPeb2+c8J8/39OnTOXr0KK+//rpTuTuc7yQZbaMnfZfTkpU4PeG8piYz7faE77ErsnLO3fl3d3Zzh++4krgsMAyDuXPnMm/ePBYuXIjdbs/yPsPCwjh+/Hiy8hMnThAWFkaxYsWIjY0lJiYmxfdzS3ptz2qc06ZNo3fv3snKCxQoQEhICGD+z65ly5Y88sgjfPvtt1lojetyqt3Fixd33FcREhLCRx99xA8//EBkZCSQ8s9Fbp7znDzfmzdvZtSoUXz11VcEBAQ4vWf1+b5eRtvoKd/l9GQ2Tk85r6nJTLvd/Xvsqqz8bLrr7+6c4A7fcSVxWTB16lQ6depEwYIF6datG5MmTcryPuvVq8e2bduc5s45fPgwgYGBhIWF4ePjQ506ddiyZYvTdps3b+bee+/N8vFdlV7bsxLngQMHOHHiBE2bNnUplsTERPz9/TPWgEzKyXZfz2634+vr6/iDcO+997J58+Ys7TMrcqrdhw4d4oknnuCrr75K9j/31OTm+b5eRtvoKd/l9GQmTk86r6nJjvPjbt9jV2W27e78uzsnuMV3PFvGuHqRc+fOGXPmzDEWLFhg/Pbbb07vHTp0yJg9e7bxxRdfOM0Tl8SVKUYMwzDatGnjmHcmPj7eeOSRR4wxY8Y43v/qq6+MWrVqGZGRkYZhmPPOVKtWzUhMTMxCy9KX0bZnNs7+/fsbb7/9dorv/fPPP4bNZnO8Xrp0qVGqVCnj1KlTWWhZ2nKj3UePHnU8j4yMNJ5++mmjU6dOjrKdO3ca5cuXd8wvtXXrViM8PNyIjo7OrmYmk9PtPnv2rHH77bcbK1euTDUGK853WjLaRnf9LmdURuL0xPOamoyeH3f8HmdWZn423e13d1a58jfb6u+4krgMOnfunPH4448bq1evTvH9DRs2OM2ddb0KFSo4fckNwzzpbdu2Nf79919H2YULF4yOHTsalStXNipVqmQMHjw42Qn/+OOPjcqVKxtVq1Y1GjdubPz9999Zb1w6MtP2tOJMqe3Xrl0zSpUqZZw4cSLFY8ycOdOoWLGicddddxl33XWX0aFDB+P333/PngamIjfa3apVK+POO+80qlevblSvXt14++23k00AumjRIqNatWrGXXfdZdStW9fYvXt39jb0Bjnd7g8//NDInz+/o81Jj2effdaxjRXnOz2ptdGTvsuZ4Wq7PfW8piYj59sdv8dZkZG2u+Pv7qy68W+2O37HfQzjhvUhJE3nz5/ntttuo0aNGqnW+e2339i9ezflypXLvcBygbe2Xe32rnaLiHgKJXEiIiIiHkgDG0REREQ8kJI4EREREQ+kJE5ERETEAymJExEREfFASuJEREREPJCSOBEREREPpCRORDxakyZN+PXXX12uv23bNtq1a5eDEYmI5A4lcSLitrZt20bVqlWdHkWLFuXzzz931LHZbNhsNsfrIUOGUKlSJadH5cqVee+99xz14+PjXTr+kiVLWLlypeN1dHQ0r776KtWqVaN69epUr16d9957z+X9eYpVq1axcOFCq8MQkXQoiRMRt3Xfffexf/9+p0fFihUZMmSII6nbuXOn0zajRo3i0KFDTo+lS5eyYsWKDB37zJkzLF26lDZt2gBw5coV6tevj7+/Pzt37uTXX3/lhx9+4I8//qBt27ZkdN70SZMmERUVlaFtckurVq1YtmwZp0+ftjoUEUmDkjgR8RgLFy4kNDSUY8eOOZK6e+65J93tgoKC8PPzy9CxxowZQ69evRyvp02bxu233857771Hvnz5AAgJCWHWrFmcP38+wz1XY8eO5ezZsxnaJjf17NmTMWPGWB2GiKRBSZyIeIRVq1YxfPhwJk+enOFto6KiCAkJcbl+fHw8a9asoUmTJo6yyZMnM2jQoGR1fX19GThwIHPnznWUVaxYkePHjzvVu+222zh58iQbN24kIiKCU6dO0aZNGx577DFHncOHD9O0aVPKly9P9erVGTZsmOO98ePHU7FiRSpVqkS1atWcLin/9ddf1KlTh+HDh1OlShUqV67M1KlTOX78OI0bN6ZatWo0bNiQP/74wymmjz/+mIoVK3LnnXfSrFkzjhw54nivWbNmrFmzJs9dKhbJS/ytDkBEJC3nz59n+PDhbN++nW+//ZbNmzfTqlUrx/vXJx7XW7lyJXv37mX48OGcPHmSMmXKuHzMbdu2Ubt2bXx8fAAzCTxy5EiqvX516tRhwIABjtdxcXHJkp+4uDhsNhsPPfQQe/fupVy5cqxcuZLbb78dgBMnTtCsWTPmzp1Lw4YNnbadPHkyS5YsYevWrRQvXpyjR4/y8MMPExoaSrNmzfDz82PXrl088sgjHDhwgNjYWO6//34WLVrE+PHjqVGjBlu3bqVv375s2LABgC+//JI1a9awe/duChQowBdffEHXrl358ccfAfDx8aF27dps27aNRo0aufzZiUjuUU+ciLit6dOnU6NGDcLCwvjpp5+oUKEC3bt3d7pHrmPHjhQqVCjZthcvXnRcrky6ly7Jpk2bqFSpEu3bt0/xuIcOHaJy5cqO11FRUYSGhhIYGJhi/VKlShEZGZmVpjJixAgGDBiQLIED8z6/yZMnU7x4cQDKlSvHe++9x/vvv++oExAQwKuvvgpA/vz5ady4MREREdSoUQOA+++/36kn7sMPP2TcuHEUKFAAgA4dOnDhwgWOHTvmqHPnnXdy+PDhLLVLRHKOeuJExG09/PDDdOnShYIFC6ZaZ86cOenu59lnn8Xf/79fdw888ACrVq1Ktf6lS5ccCRNAoUKFuHjxIvHx8Skmcv/++2+GLtem5Mcff+TZZ59NVh4ZGcnFixcdyViS+++/n549ezpeFytWzKmN+fPn57bbbnPaJqlnEeD333+nS5cuTmWxsbGcP3+eW265BYCiRYty/vz5LLVLRHKOkjgRcVvh4eEAdO/enV9++SXFOufOnWPXrl2OuikpWrQoly9fZteuXVy7di3d4xYtWpRLly45XhcuXJiwsDB27tzJfffdl6z+jh07qFKlSpr7vHr1arrHTUhISFbm65vyBRPDMNIdrJFazyGYCdv69espUaJEqnUuXbpEaGhomscQEesoiRMRt3f9oIEb3X///Zw9e5bw8HCmT5/O9OnTAfNeusjISDZt2oSvry/FixenQoUKPPDAA+ker2LFinz22WdOZV26dGH8+PHJkji73c7HH3/Mk08+6SgrUqQI58+fd9zvduzYMaekEEiWgNWrV4/169dTt25dp/JChQpRpkwZdu/eTc2aNR3l27ZtS9Y7lxEVKlRg586dtGzZMtU6Bw8epHv37pk+hojkLN0TJyJur0+fPpQvXz7ZxL9Vq1YFzHvEAHr06MHatWtZu3Ytv/32G+fPn2f//v389ttvbNy4kalTp1KqVKl0j3ffffexfft2p7nfhg4dyoEDBxg8eDCxsbGAea9cz5498fX1dboUWqdOHUfimZCQwJAhQ5wuzwLcdNNNTvefDRs2jIkTJ/Ldd98li+eVV16hb9++jnv8jhw5wtChQxk8eLArH1+KevbsyZAhQzh58iRg9uxdH49hGGzfvj3FnkcRcQ/qiRMRt3fo0CGWLFnC3XffnWa9wMBAbrrppiwfLygoiObNm/Ptt9/SokULwJwT7ocffuDNN9+kRo0aBAYGYrPZaN26NRMmTCAgIMCx/YgRI3j66ae566678Pf354UXXuDo0aNOdV544QV69epFmTJlmDt3Lrfffjvr1q3j+eefp3///gQEBNCsWTNGjx5N7969sdlsjkEPQUFBjBw5koceeggwBzUkzV13/Wdx/fEAxyAGgIEDB+Lj40Pz5s3x8fHBz8+PJ5980jGNypo1a2jevHmal2RFxFo+RkanGRcRyWV9+vRh3bp1BAcHp/j+wIED6d27t0v72rZtG+PGjWPZsmVp1jt9+jR9+vRh+fLlGQ03T3j00UeZPHkypUuXtjoUEUmFkjgRkVQsXryYgIAA2rVrZ3UouWrFihVcuXKFLl26WB2KiKRBSZyIiIiIB9LABhEREREPpCRORERExAMpiRMRERHxQEriRERERDyQkjgRERERD6QkTkRERMQDKYkTERER8UBK4kREREQ80P8B+lY+V3LDDOwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png]()\n",
        "\n",
        "\n",
        "3. 이론적 적합성\n",
        "  - 로그 함수를 사용하는 것이 이론적으로 맞는지 여부는 다음과 같은 이유로 생각해볼 수 있습니다:\n",
        "\n",
        "  - 이득과 손실에 대한 감수성: 로그 함수는 이득과 손실의 크기에 따라 점점 둔화되는 증가 또는 감소를 나타내므로, 초기 이득이나 손실에 대해 더 민감하게 반응하는 특성을 가질 수 있습니다. 하지만 이득과 손실에 대한 민감도가 원래 전망이론에서 가정한\n",
        "𝑥\n",
        "𝛼\n",
        "x\n",
        "α\n",
        "  또는\n",
        "−\n",
        "𝜆\n",
        "(\n",
        "−\n",
        "𝑥\n",
        ")\n",
        "𝛽\n",
        "−λ(−x)\n",
        "β\n",
        "  함수보다는 약할 수 있습니다.\n",
        "손실 회피: 로그 함수는 원래의 전망이론 가치 함수가 손실 회피를 더 강하게 반영할 수 있는 것에 비해, 손실에 대해 더 완만한 반응을 보일 수 있습니다.\n",
        "\n",
        "4. 결론\n",
        "  - 로그 함수는 가치 함수로 사용될 수 있으며, 특히 작은 변화에 대한 감수성을 강조할 때 유용할 수 있습니다.\n",
        "그러나 전망이론의 원래 가치 함수가 비선형성을 더 잘 반영하는 측면에서는\n",
        "𝑥\n",
        "𝛼\n",
        "x\n",
        "α\n",
        " 와\n",
        "−\n",
        "𝜆\n",
        "(\n",
        "−\n",
        "𝑥\n",
        ")\n",
        "𝛽\n",
        "−λ(−x)\n",
        "β\n",
        "  형태가 더 이론에 부합할 가능성이 높습니다.\n",
        "따라서 로그 함수는 특정 상황에서 유용할 수 있지만, 전망이론의 전통적인 구조에서는 원래의 함수가 더 잘 맞을 수 있습니다."
      ],
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      "source": [
        "####보험 가입 사례\n",
        "사람들은 재난이나 사고와 같은 낮은 확률의 이벤트에 대비해 보험에 가입합니다.\n",
        "예를 들어, 미국에서는 주택 소유자들이 허리케인 보험에 가입하는 경우가 많습니다.\n",
        "허리케인이 발생할 확률은 매우 낮지만, 발생 시 손해가 매우 크기 때문에 사람들은 이 손실을 과대평가합니다.\n",
        "따라서 높은 보험료를 기꺼이 지불하게 됩니다.\n",
        "이러한 행동은 전망이론에서 설명하는 낮은 확률의 과대평가에 해당합니다.\n",
        "\n",
        "####복권 구매 사례\n",
        "복권은 매우 낮은 확률로 큰 금액을 당첨받을 수 있는 도박입니다.\n",
        "예를 들어, 메가 밀리언즈(Mega Millions) 복권의 당첨 확률은 약 3억 분의 1입니다.\n",
        "하지만 많은 사람들이 매주 복권을 구매합니다.\n",
        "이는 사람들 대부분이 낮은 확률의 큰 이익을 과대평가하기 때문입니다.\n",
        "전망이론에 따르면, 이들은 실제 확률보다 당첨될 가능성을 훨씬 더 높게 인식합니다.\n",
        "결과적으로 비합리적인 기대감을 가지고 복권을 구매하는 행동이 나타납니다.\n",
        "\n",
        "####투자 결정 사례\n",
        "주식 시장에서 투자자들의 행동은 전망이론을 잘 보여줍니다.\n",
        "예를 들어, 한 투자자가 주식을 구매한 후 주가가 떨어졌다고 가정해봅시다.\n",
        "이 투자자는 손실을 보고 매도하지 않고 계속 보유하려는 경향이 있습니다.\n",
        "이는 손실을 확정 짓는 것보다 잠재적인 회복 가능성을 더 선호하는 심리적 경향에서 비롯됩니다.\n",
        "반대로, 주가가 올라 이익이 발생한 경우, 투자자들은 너무 일찍 주식을 매도하여 이익을 확정하려는 경향이 있습니다.\n",
        "이러한 행동은 이익보다 손실을 더 크게 느끼는 '손실 회피' 성향 때문입니다.\n",
        "결국, 투자자들은 합리적인 판단을 하지 못하고, 비합리적으로 행동하게 됩니다.\n",
        "\n",
        "####협상 상황 사례\n",
        "협상에서도 전망이론은 중요한 역할을 합니다.\n",
        "예를 들어, 직장에서의 연봉 협상을 생각해봅시다.\n",
        "직원이 10% 인상된 연봉을 기대하고 있지만, 실제로는 5% 인상 제안을 받는다면 이는 예상보다 낮은 결과로 인식되어 손실로 여겨질 수 있습니다.\n",
        "결과적으로 직원은 이 제안을 거절하고 더 많은 인상을 요구할 수 있습니다.\n",
        "반면에, 아무런 기대 없이 연봉 협상에 임한 직원이 동일한 5% 인상 제안을 받는다면, 이는 이익으로 인식되어 쉽게 받아들일 가능성이 높습니다.\n",
        "이처럼 같은 제안이더라도 개인의 기대와 상황에 따라 그 반응이 달라지는 것은 전망이론에서 설명하는 가치 함수와 관련이 있습니다.\n",
        "\n",
        "####스포츠 베팅 사례\n",
        "스포츠 베팅에서도 전망이론이 적용됩니다.\n",
        "예를 들어, 사람들이 약한 팀이 강한 팀을 이길 가능성이 낮지만 높은 배당금을 제공하는 베팅에 돈을 걸 때, 이들은 낮은 확률의 성공을 과대평가하고 있습니다.\n",
        "반대로, 승리할 가능성이 높은 팀에 돈을 걸 경우, 이들은 낮은 배당금을 과소평가할 수 있습니다.\n",
        "이런 비합리적인 베팅 행태는 사람들의 확률 왜곡과 가치 판단의 비대칭성에서 비롯됩니다.\n",
        "\n",
        "####정리\n",
        "전망이론은 실제 사람들이 의사결정을 할 때 나타나는 비합리적인 행동을 잘 설명합니다.\n",
        "이론이 적용되는 사례는 다양하며, 보험 가입, 복권 구매, 투자 결정, 협상, 스포츠 베팅 등에서 나타납니다.\n",
        "이런 사례들은 모두 사람들이 확률을 왜곡하여 인식하거나, 이득과 손실을 다르게 평가하는 경향을 보여줍니다.\n",
        "이러한 행동들은 전통적인 경제학 이론으로는 설명하기 어려운 부분을 설명해줍니다."
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